A neural network approach to FIR filter design using frequency-response masking technique

Authors:
Xiao-Hua Wang;Yi-Gang He
Affiliations:
Department of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan 410077, China and Department of Electrical and Information Engineering, Hunan U ...;Department of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China
Venue:
Signal Processing
Year:
2008

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Abstract

This paper presents a new optimization method for the design of various frequency-response-masking (FRM)-based linear-phase finite-impulse response (FIR) digital filters. The method is based on a batch back-propagation neural network algorithm (NNA), which is taken as a variable learning rate mode. In order to reduce the complexity, the following two-step optimization technique is proposed. At the first step, an initial FRM filter is designed by alternately optimizing the sub-filters. This solution is then used as a start-up solution for further optimization. At the second step, the coefficients of overall sub-filters are optimized simultaneously by the NNA. Algorithm details for the design of basic and multistage FRM filters are presented to show that the proposed approach offers a unified design framework for a variety of FRM filters. Some examples taken from the literatures are included and the results show that the proposed algorithm can design better FRM filters than several existing methods.